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3
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Part 1: Overview of the Probably Approximately Correct (PAC) Learning Framework
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16
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Probabilistic Analysis of Learning in Artificial Neural Networks: The PAC Model and its Variants
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182
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Efficient Distribution-free Learning of Probabilistic Concepts
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6
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Efficient Learning from Faulty Data
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37
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Probably Approximately Correct Learning
– David Haussler
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Computational Learning Theory
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11
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Learning by Canonical Smooth Estimation, Part I: Simultaneous Estimation
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2
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Faithful Representations and Moments of Satisfaction: Probabilistic Methods in Learning and Logic
– Lidror Troyansky, Prof Naftali Tishby
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4
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Agnostic Learning and Single Hidden Layer Neural Networks
– Wee Sun Lee
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!()+, -./01 23456
– Department Of Computer, David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass, Technische Universitaet Graz
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We Will Give a Reduction Showing How Algorithm
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4
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On the Learnability of Discrete Distributions (Extended Abstract)
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2
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Knowledge acquisition in statistical learning theory
– Shai Fine
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248
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Efficient noise-tolerant learning from statistical queries
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78
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On the learnability of discrete distributions
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18
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Data Filtering and Distribution Modeling Algorithms for Machine Learning
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1
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P-sufficient statistics for PAC learning k-term-DNF formulas through enumeration.
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Lower Bounds in . . . Learning Theory via Analytic Methods
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The Fourier Transform in Computational Learning Theory
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